AI Enters Lending and Investment Operations in Private Markets

A startup is working to transform private markets (the investment and lending sector for unlisted companies) by introducing AI technology into its operations. The wave of AI-driven efficiency improvements is beginning to reach this field, which has historically relied heavily on manual work by specialized professionals.

Private markets refer to investments and lending in companies or assets that are not listed on stock exchanges. Specifically, this includes private equity (investments in unlisted shares) and private credit (direct lending without intermediation by banks), among others. These fields require vast amounts of documentation and human judgment for tasks such as due diligence (detailed pre-investment investigation), lending review, and contract analysis. Consequently, the barriers to entry are high, and the market has been dominated by large financial institutions and select specialist funds.

The focus of recent attention is the movement by startups aiming to replace or supplement these labor-intensive tasks with AI. According to the source material, the startup in question claims that "AI technology can transform private markets investment and lending operations." However, specific details about service offerings, implementation results, and the company name are limited to what can be verified from the source, so it is appropriate to characterize this at present as "a move toward transformation."

In the context of AI being applied to financial operations, the ability to analyze large volumes of text data (financial documents, contracts, performance reports, etc.) in a short time is considered particularly valuable. In private markets, unlike listed companies, publicly available information is limited, making the collection, organization, and analysis of scattered non-public data particularly cumbersome. By applying AI to address these challenges, it becomes possible to compress both the time and cost required for review.

At the same time, AI adoption in private markets faces distinct challenges. Most transactions are non-standardized, and data formats and quality vary from deal to deal, making it difficult to directly apply general-purpose AI models in many cases. Furthermore, lending and investment decisions involve legal risks and credit assessment, leaving unresolved questions about trust and reliability regarding how far AI judgment can be incorporated into actual practice. For these reasons, the more realistic approach is to position AI as a "tool to assist human experts" rather than pursue complete automation.

In recent years, private markets have been opening not only to institutional investors but also to wealthy individuals, and as the market grows, increasing transaction volumes have become a challenge. It is in this growth phase that the demand for AI application can naturally emerge. The movement of startups entering this space can also be viewed as a "litmus test" for measuring the pace at which established large financial institutions are advancing their own AI transformation.

Key points to watch going forward include how extensively these AI tools will be integrated into actual investment and lending decisions, and how regulators will position AI-assisted financial reviews. Globally, regulatory discussion around AI in finance is advancing, and private markets are no exception. The balance between the speed of technological adoption and the pace of regulatory framework development will likely determine the future trajectory of this sector.